Abstract
Recommender system plays a supporting role in the process of information filtering. It plays a remarkable role in large-scale online shopping and product suggestions. This paper discusses various trends of recommender system such as content-based, collaborative-based and hybrid personalization techniques proposed for recommendations. It provides better insight and future directions of recommender systems. We have reviewed 142 articles from several journals and conference papers which were published from 1992 to 2019. We have used statistical descriptions to show the progression and drawbacks of the various notions of recommendation approaches. We have also discussed growing research demand in the area of recommender systems as well as the pros and cons of the currently available classifications. We have created a classification of recommender techniques, including various user inputs, knowledge from the database, the ways in which the recommendation will be presented to the user and the technologies which are used to create the recommendations.
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Sinha, B.B., Dhanalakshmi, R. Evolution of recommender system over the time. Soft Comput 23, 12169–12188 (2019). https://doi.org/10.1007/s00500-019-04143-8
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DOI: https://doi.org/10.1007/s00500-019-04143-8